JUREX-4E: Juridical Expert-Annotated Four-Element Knowledge Base for Legal Reasoning

ACL ARR 2025 May Submission2618 Authors

Abstract:

In recent years, Large Language Models (LLMs) have been widely applied to legal tasks. To enhance their understanding of legal texts and improve reasoning accuracy, a promising approach is to incorporate legal theories. One of the most widely adopted framework is the Four-Element Theory (FET), which defines the crime constitution through four elements: Subject, Object, Subjective Aspect, and Objective Aspect. While recent work has explored prompting LLMs to follow FET, our evaluation demonstrates that LLM-generated four-elements are often incomplete and less representative, limiting their effectiveness in legal reasoning.
To address these issues, we present JUREX-4E, an expert-annotated four-elements knowledge base covering 155 criminal charges. The annotations follow a progressive hierarchical framework grounded in legal source validity and incorporate diverse interpretive methods to ensure precision and authority.
We evaluate JUREX-4E on the Similar Charge Distinction task and apply it to Legal Case Retrieval. Experimental results validate the high quality of JUREX-4E and its substantial impact on downstream legal tasks, underscoring its potential for advancing legal AI applications. The dataset and code are available at: \url{https://anonymous.4open.science/r/JUREX-86B9/}

Paper Type: Long

Research Area: Special Theme (conference specific)

Research Area Keywords: legal NLP

Contribution Types: Model analysis & interpretability, Data resources

Languages Studied: Chinese

Submission Number: 2618